
## Introduction
GridEX (short for “Gridded EXposure”) is a grid-based measure of exposure to extreme heat and cold events with a high spatial resolution of 500x500 for the contiguous United States, spanning from 2008 to 2022. GridEx leverages a data science pipeline to gather station-based climatological data and estimate fine-resolution surfaces of extreme events intensity and the aggreagted summaries at administrative and census boundarie.

For the latest information and resources for accessing GridEX dataset please visit the Confluence Project website at:
https://www.confluence-project.org/gridex.html

--------------------------------------------------------------------------------

## Data Types and File Formats
The GridEX’s raster layers are delivered as gridded data in GeoTiff format, and the vector layers of EHE/ECE boundaries are provided as GeoPackage files. All the spatial computations were conducted based on the EPSG:5070 projected coordinate system for United States (USA). In addition, for the aggreagte results presented at administrative and census boundaries, the provided spatial layers were transformed to EPSG:4269 which is a geographic coordinate system based on latitude and longitude coordinates.

This dataset provides a catalog of extreme heat and cold events in multiple spatial and temporal scales and different data models.
To facilitate the data identification and organization, we have implemented a specific naming convention. This convention uses a three-digit code prefixed with "S" to denote the spatial scale, followed by another three-digit code prefixed with "T" to indicate the temporal scale. For a detailed reference of GridEX naming convention and their corresponding spatial and temporal scales, please review the provided naming convention (GridEX Naming Convention - PAA.pdf).

--------------------------------------------------------------------------------

## Sample Data Processing Workflow
Users can process the precomputed EHE/ECE dataset using standard workstations equipped with 8 cores and 16 GB of memory. A comprehensive set of Quarto notebooks is available to help users start loading and processing this dataset. This collection is accessible at:
https://github.com/epedram/us_ehe_ece/tree/main/PAA2024/Session2

